Overview

Brought to you by YData

Dataset statistics

Number of variables50
Number of observations65249
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.9 MiB
Average record size in memory94.2 B

Variable types

Boolean34
Categorical4
Numeric12

Alerts

other_animals has a high cardinality: 330 distinct values High cardinality
accessible_by_public_transport is highly overall correlated with car_requiredHigh correlation
attraction_city is highly overall correlated with attraction_countrysideHigh correlation
attraction_countryside is highly overall correlated with attraction_cityHigh correlation
car_required is highly overall correlated with accessible_by_public_transportHigh correlation
days_since_modified is highly overall correlated with nb_assignments_filled and 4 other fieldsHigh correlation
family_friendly is highly overall correlated with welcomes_familyHigh correlation
id is highly overall correlated with nb_of_photos and 1 other fieldsHigh correlation
minutes_pet_can_be_left_alone is highly overall correlated with pets_welcomeHigh correlation
nb_assignments_filled is highly overall correlated with days_since_modified and 4 other fieldsHigh correlation
nb_assignments_published is highly overall correlated with days_since_modified and 4 other fieldsHigh correlation
nb_distinct_pets is highly overall correlated with nb_of_pets and 1 other fieldsHigh correlation
nb_domestic_sitters is highly overall correlated with days_since_modified and 4 other fieldsHigh correlation
nb_of_pets is highly overall correlated with nb_distinct_petsHigh correlation
nb_of_photos is highly overall correlated with id and 4 other fieldsHigh correlation
nb_repeat_sitters is highly overall correlated with nb_assignments_filled and 3 other fieldsHigh correlation
nb_unique_sitters is highly overall correlated with days_since_modified and 4 other fieldsHigh correlation
pet_cat is highly overall correlated with pet_dogHigh correlation
pet_dog is highly overall correlated with pet_catHigh correlation
pet_poultry is highly overall correlated with nb_distinct_petsHigh correlation
pets_welcome is highly overall correlated with days_since_modified and 1 other fieldsHigh correlation
photo_exterior is highly overall correlated with nb_of_photos and 2 other fieldsHigh correlation
photo_garden is highly overall correlated with nb_of_photos and 2 other fieldsHigh correlation
photo_interior is highly overall correlated with nb_of_photos and 2 other fieldsHigh correlation
welcomes_any_age is highly overall correlated with welcomes_baby and 4 other fieldsHigh correlation
welcomes_baby is highly overall correlated with welcomes_any_age and 4 other fieldsHigh correlation
welcomes_child is highly overall correlated with welcomes_any_age and 4 other fieldsHigh correlation
welcomes_family is highly overall correlated with family_friendlyHigh correlation
welcomes_teen is highly overall correlated with welcomes_any_age and 4 other fieldsHigh correlation
welcomes_toddler is highly overall correlated with welcomes_any_age and 4 other fieldsHigh correlation
welcomes_young is highly overall correlated with welcomes_any_age and 4 other fieldsHigh correlation
year_approved is highly overall correlated with id and 1 other fieldsHigh correlation
car_included is highly imbalanced (53.1%) Imbalance
home_type is highly imbalanced (51.0%) Imbalance
wish_to_meet_in_person is highly imbalanced (86.4%) Imbalance
wish_to_video_call is highly imbalanced (94.8%) Imbalance
other_animals is highly imbalanced (98.7%) Imbalance
pet_bird is highly imbalanced (89.0%) Imbalance
pet_fish is highly imbalanced (72.4%) Imbalance
pet_reptile is highly imbalanced (88.6%) Imbalance
pet_poultry is highly imbalanced (67.3%) Imbalance
pet_farm_animal is highly imbalanced (88.1%) Imbalance
photo_interior is highly imbalanced (68.3%) Imbalance
photo_exterior is highly imbalanced (67.9%) Imbalance
photo_attraction is highly imbalanced (93.8%) Imbalance
photo_garden is highly imbalanced (67.9%) Imbalance
photo_pool is highly imbalanced (96.1%) Imbalance
photo_view is highly imbalanced (94.4%) Imbalance
welcomes_single is highly imbalanced (86.6%) Imbalance
welcomes_couple is highly imbalanced (86.5%) Imbalance
welcomes_any_age is highly imbalanced (58.0%) Imbalance
welcomes_baby is highly imbalanced (72.6%) Imbalance
welcomes_toddler is highly imbalanced (67.1%) Imbalance
welcomes_child is highly imbalanced (61.3%) Imbalance
welcomes_teen is highly imbalanced (61.5%) Imbalance
welcomes_young is highly imbalanced (63.9%) Imbalance
nb_invites is highly skewed (γ1 = 63.0894506) Skewed
id has unique values Unique
nb_assignments_filled has 12714 (19.5%) zeros Zeros
nb_assignments_published has 3676 (5.6%) zeros Zeros
nb_domestic_sitters has 18000 (27.6%) zeros Zeros
nb_invites has 26043 (39.9%) zeros Zeros
nb_of_photos has 54290 (83.2%) zeros Zeros
nb_repeat_sitters has 46198 (70.8%) zeros Zeros
nb_unique_sitters has 12714 (19.5%) zeros Zeros

Reproduction

Analysis started2025-03-21 08:10:12.241388
Analysis finished2025-03-21 08:10:51.619813
Duration39.38 seconds
Software versionydata-profiling vv4.14.0
Download configurationconfig.json

Variables

accessible_by_public_transport
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size573.5 KiB
True
34521 
False
30728 
ValueCountFrequency (%)
True 34521
52.9%
False 30728
47.1%
2025-03-21T08:10:51.700855image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1019.5 KiB
0
44171 
1
21078 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters65249
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 44171
67.7%
1 21078
32.3%

Length

2025-03-21T08:10:51.830011image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-21T08:10:51.960814image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 44171
67.7%
1 21078
32.3%

Most occurring characters

ValueCountFrequency (%)
0 44171
67.7%
1 21078
32.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 65249
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 44171
67.7%
1 21078
32.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 65249
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 44171
67.7%
1 21078
32.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 65249
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 44171
67.7%
1 21078
32.3%

car_included
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size573.5 KiB
False
58731 
True
6518 
ValueCountFrequency (%)
False 58731
90.0%
True 6518
 
10.0%
2025-03-21T08:10:52.082696image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

car_required
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size573.5 KiB
False
33584 
True
31665 
ValueCountFrequency (%)
False 33584
51.5%
True 31665
48.5%
2025-03-21T08:10:52.208461image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size573.5 KiB
False
54907 
True
10342 
ValueCountFrequency (%)
False 54907
84.1%
True 10342
 
15.9%
2025-03-21T08:10:52.331431image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

family_friendly
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size573.5 KiB
False
46131 
True
19118 
ValueCountFrequency (%)
False 46131
70.7%
True 19118
29.3%
2025-03-21T08:10:52.447301image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

home_type
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size573.6 KiB
house
52216 
apartment
12421 
unknown
 
612

Length

Max length9
Median length5
Mean length5.7802112
Min length5

Characters and Unicode

Total characters377153
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowapartment
2nd rowhouse
3rd rowapartment
4th rowhouse
5th rowhouse

Common Values

ValueCountFrequency (%)
house 52216
80.0%
apartment 12421
 
19.0%
unknown 612
 
0.9%

Length

2025-03-21T08:10:52.614662image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-21T08:10:52.767139image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
house 52216
80.0%
apartment 12421
 
19.0%
unknown 612
 
0.9%

Most occurring characters

ValueCountFrequency (%)
e 64637
17.1%
o 52828
14.0%
u 52828
14.0%
h 52216
13.8%
s 52216
13.8%
a 24842
 
6.6%
t 24842
 
6.6%
n 14257
 
3.8%
p 12421
 
3.3%
r 12421
 
3.3%
Other values (3) 13645
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 377153
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 64637
17.1%
o 52828
14.0%
u 52828
14.0%
h 52216
13.8%
s 52216
13.8%
a 24842
 
6.6%
t 24842
 
6.6%
n 14257
 
3.8%
p 12421
 
3.3%
r 12421
 
3.3%
Other values (3) 13645
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 377153
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 64637
17.1%
o 52828
14.0%
u 52828
14.0%
h 52216
13.8%
s 52216
13.8%
a 24842
 
6.6%
t 24842
 
6.6%
n 14257
 
3.8%
p 12421
 
3.3%
r 12421
 
3.3%
Other values (3) 13645
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 377153
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 64637
17.1%
o 52828
14.0%
u 52828
14.0%
h 52216
13.8%
s 52216
13.8%
a 24842
 
6.6%
t 24842
 
6.6%
n 14257
 
3.8%
p 12421
 
3.3%
r 12421
 
3.3%
Other values (3) 13645
 
3.6%

id
Real number (ℝ)

High correlation  Unique 

Distinct65249
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean704371.69
Minimum201
Maximum1180911
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size764.6 KiB
2025-03-21T08:10:52.927142image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum201
5-th percentile112220.6
Q1480518
median749694
Q3948180
95-th percentile1129577.4
Maximum1180911
Range1180710
Interquartile range (IQR)467662

Descriptive statistics

Standard deviation310280.76
Coefficient of variation (CV)0.44050715
Kurtosis-0.6667021
Mean704371.69
Median Absolute Deviation (MAD)210024
Skewness-0.52625578
Sum4.5959548 × 1010
Variance9.6274151 × 1010
MonotonicityNot monotonic
2025-03-21T08:10:53.101049image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201 1
 
< 0.1%
938803 1
 
< 0.1%
938183 1
 
< 0.1%
938246 1
 
< 0.1%
938253 1
 
< 0.1%
938272 1
 
< 0.1%
938990 1
 
< 0.1%
938353 1
 
< 0.1%
938355 1
 
< 0.1%
938382 1
 
< 0.1%
Other values (65239) 65239
> 99.9%
ValueCountFrequency (%)
201 1
< 0.1%
418 1
< 0.1%
439 1
< 0.1%
776 1
< 0.1%
939 1
< 0.1%
1033 1
< 0.1%
1041 1
< 0.1%
1052 1
< 0.1%
1095 1
< 0.1%
1310 1
< 0.1%
ValueCountFrequency (%)
1180911 1
< 0.1%
1180857 1
< 0.1%
1180818 1
< 0.1%
1180813 1
< 0.1%
1180765 1
< 0.1%
1180669 1
< 0.1%
1180660 1
< 0.1%
1180616 1
< 0.1%
1180602 1
< 0.1%
1180570 1
< 0.1%

minutes_pet_can_be_left_alone
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1019.5 KiB
120
47947 
360
6358 
240
6337 
480
 
4464
0
 
143

Length

Max length3
Median length3
Mean length2.9956168
Min length1

Characters and Unicode

Total characters195461
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row360
2nd row240
3rd row120
4th row240
5th row240

Common Values

ValueCountFrequency (%)
120 47947
73.5%
360 6358
 
9.7%
240 6337
 
9.7%
480 4464
 
6.8%
0 143
 
0.2%

Length

2025-03-21T08:10:53.285382image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-21T08:10:53.449728image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
120 47947
73.5%
360 6358
 
9.7%
240 6337
 
9.7%
480 4464
 
6.8%
0 143
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 65249
33.4%
2 54284
27.8%
1 47947
24.5%
4 10801
 
5.5%
3 6358
 
3.3%
6 6358
 
3.3%
8 4464
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 195461
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 65249
33.4%
2 54284
27.8%
1 47947
24.5%
4 10801
 
5.5%
3 6358
 
3.3%
6 6358
 
3.3%
8 4464
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 195461
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 65249
33.4%
2 54284
27.8%
1 47947
24.5%
4 10801
 
5.5%
3 6358
 
3.3%
6 6358
 
3.3%
8 4464
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 195461
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 65249
33.4%
2 54284
27.8%
1 47947
24.5%
4 10801
 
5.5%
3 6358
 
3.3%
6 6358
 
3.3%
8 4464
 
2.3%

nb_assignments_filled
Real number (ℝ)

High correlation  Zeros 

Distinct88
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7170378
Minimum0
Maximum157
Zeros12714
Zeros (%)19.5%
Negative0
Negative (%)0.0%
Memory size764.6 KiB
2025-03-21T08:10:53.737842image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q38
95-th percentile20
Maximum157
Range157
Interquartile range (IQR)7

Descriptive statistics

Standard deviation7.3734882
Coefficient of variation (CV)1.2897393
Kurtosis19.828042
Mean5.7170378
Median Absolute Deviation (MAD)3
Skewness3.0320994
Sum373031
Variance54.368328
MonotonicityNot monotonic
2025-03-21T08:10:53.945044image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12714
19.5%
1 10140
15.5%
2 6392
9.8%
3 4840
 
7.4%
4 4249
 
6.5%
5 3596
 
5.5%
6 3095
 
4.7%
7 2703
 
4.1%
8 2294
 
3.5%
9 2033
 
3.1%
Other values (78) 13193
20.2%
ValueCountFrequency (%)
0 12714
19.5%
1 10140
15.5%
2 6392
9.8%
3 4840
 
7.4%
4 4249
 
6.5%
5 3596
 
5.5%
6 3095
 
4.7%
7 2703
 
4.1%
8 2294
 
3.5%
9 2033
 
3.1%
ValueCountFrequency (%)
157 1
< 0.1%
154 1
< 0.1%
140 1
< 0.1%
129 1
< 0.1%
122 1
< 0.1%
90 1
< 0.1%
89 1
< 0.1%
88 1
< 0.1%
86 1
< 0.1%
84 2
< 0.1%

nb_assignments_published
Real number (ℝ)

High correlation  Zeros 

Distinct96
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.7998897
Minimum0
Maximum161
Zeros3676
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size764.6 KiB
2025-03-21T08:10:54.154869image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q39
95-th percentile22
Maximum161
Range161
Interquartile range (IQR)8

Descriptive statistics

Standard deviation8.0320958
Coefficient of variation (CV)1.1812097
Kurtosis18.821359
Mean6.7998897
Median Absolute Deviation (MAD)3
Skewness3.0163338
Sum443686
Variance64.514562
MonotonicityNot monotonic
2025-03-21T08:10:54.374630image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 13138
20.1%
2 7694
11.8%
3 5630
 
8.6%
4 4539
 
7.0%
5 3808
 
5.8%
0 3676
 
5.6%
6 3236
 
5.0%
7 2918
 
4.5%
8 2610
 
4.0%
9 2214
 
3.4%
Other values (86) 15786
24.2%
ValueCountFrequency (%)
0 3676
 
5.6%
1 13138
20.1%
2 7694
11.8%
3 5630
8.6%
4 4539
 
7.0%
5 3808
 
5.8%
6 3236
 
5.0%
7 2918
 
4.5%
8 2610
 
4.0%
9 2214
 
3.4%
ValueCountFrequency (%)
161 1
< 0.1%
157 1
< 0.1%
154 1
< 0.1%
143 1
< 0.1%
127 1
< 0.1%
107 1
< 0.1%
100 1
< 0.1%
95 1
< 0.1%
92 1
< 0.1%
91 1
< 0.1%

nb_distinct_pets
Real number (ℝ)

High correlation 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3724961
Minimum0
Maximum8
Zeros408
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size764.6 KiB
2025-03-21T08:10:54.532490image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.71140133
Coefficient of variation (CV)0.51832666
Kurtosis6.6175048
Mean1.3724961
Median Absolute Deviation (MAD)0
Skewness2.2263807
Sum89554
Variance0.50609186
MonotonicityNot monotonic
2025-03-21T08:10:54.668091image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 46358
71.0%
2 13970
 
21.4%
3 3220
 
4.9%
4 959
 
1.5%
0 408
 
0.6%
5 258
 
0.4%
6 64
 
0.1%
7 10
 
< 0.1%
8 2
 
< 0.1%
ValueCountFrequency (%)
0 408
 
0.6%
1 46358
71.0%
2 13970
 
21.4%
3 3220
 
4.9%
4 959
 
1.5%
5 258
 
0.4%
6 64
 
0.1%
7 10
 
< 0.1%
8 2
 
< 0.1%
ValueCountFrequency (%)
8 2
 
< 0.1%
7 10
 
< 0.1%
6 64
 
0.1%
5 258
 
0.4%
4 959
 
1.5%
3 3220
 
4.9%
2 13970
 
21.4%
1 46358
71.0%
0 408
 
0.6%

nb_domestic_sitters
Real number (ℝ)

High correlation  Zeros 

Distinct72
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9536545
Minimum0
Maximum123
Zeros18000
Zeros (%)27.6%
Negative0
Negative (%)0.0%
Memory size764.6 KiB
2025-03-21T08:10:54.839601image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q35
95-th percentile15
Maximum123
Range123
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.6446707
Coefficient of variation (CV)1.4277097
Kurtosis25.140609
Mean3.9536545
Median Absolute Deviation (MAD)2
Skewness3.4178821
Sum257972
Variance31.862308
MonotonicityNot monotonic
2025-03-21T08:10:55.038224image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 18000
27.6%
1 11481
17.6%
2 7135
 
10.9%
3 5234
 
8.0%
4 4158
 
6.4%
5 3297
 
5.1%
6 2733
 
4.2%
7 2144
 
3.3%
8 1786
 
2.7%
9 1514
 
2.3%
Other values (62) 7767
11.9%
ValueCountFrequency (%)
0 18000
27.6%
1 11481
17.6%
2 7135
 
10.9%
3 5234
 
8.0%
4 4158
 
6.4%
5 3297
 
5.1%
6 2733
 
4.2%
7 2144
 
3.3%
8 1786
 
2.7%
9 1514
 
2.3%
ValueCountFrequency (%)
123 1
< 0.1%
119 1
< 0.1%
118 1
< 0.1%
117 1
< 0.1%
98 1
< 0.1%
78 1
< 0.1%
76 2
< 0.1%
69 2
< 0.1%
68 1
< 0.1%
67 1
< 0.1%

nb_invites
Real number (ℝ)

Skewed  Zeros 

Distinct333
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5428129
Minimum0
Maximum4983
Zeros26043
Zeros (%)39.9%
Negative0
Negative (%)0.0%
Memory size764.6 KiB
2025-03-21T08:10:55.245883image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q36
95-th percentile31.6
Maximum4983
Range4983
Interquartile range (IQR)6

Descriptive statistics

Standard deviation32.470212
Coefficient of variation (CV)4.3047882
Kurtosis8621.8874
Mean7.5428129
Median Absolute Deviation (MAD)1
Skewness63.089451
Sum492161
Variance1054.3147
MonotonicityNot monotonic
2025-03-21T08:10:55.424810image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 26043
39.9%
1 8362
 
12.8%
2 5107
 
7.8%
3 3779
 
5.8%
4 2909
 
4.5%
5 2249
 
3.4%
6 1878
 
2.9%
7 1444
 
2.2%
8 1273
 
2.0%
9 1024
 
1.6%
Other values (323) 11181
17.1%
ValueCountFrequency (%)
0 26043
39.9%
1 8362
 
12.8%
2 5107
 
7.8%
3 3779
 
5.8%
4 2909
 
4.5%
5 2249
 
3.4%
6 1878
 
2.9%
7 1444
 
2.2%
8 1273
 
2.0%
9 1024
 
1.6%
ValueCountFrequency (%)
4983 1
< 0.1%
1182 1
< 0.1%
1177 1
< 0.1%
1156 1
< 0.1%
1084 1
< 0.1%
836 1
< 0.1%
831 1
< 0.1%
762 1
< 0.1%
754 1
< 0.1%
748 1
< 0.1%

nb_of_pets
Real number (ℝ)

High correlation 

Distinct34
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1223161
Minimum0
Maximum51
Zeros408
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size764.6 KiB
2025-03-21T08:10:55.584768image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile5
Maximum51
Range51
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6392138
Coefficient of variation (CV)0.77237024
Kurtosis57.575318
Mean2.1223161
Median Absolute Deviation (MAD)1
Skewness4.5916719
Sum138479
Variance2.6870218
MonotonicityNot monotonic
2025-03-21T08:10:55.746403image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
1 26973
41.3%
2 21240
32.6%
3 8516
 
13.1%
4 3917
 
6.0%
5 1825
 
2.8%
6 1006
 
1.5%
7 479
 
0.7%
0 408
 
0.6%
8 275
 
0.4%
9 202
 
0.3%
Other values (24) 408
 
0.6%
ValueCountFrequency (%)
0 408
 
0.6%
1 26973
41.3%
2 21240
32.6%
3 8516
 
13.1%
4 3917
 
6.0%
5 1825
 
2.8%
6 1006
 
1.5%
7 479
 
0.7%
8 275
 
0.4%
9 202
 
0.3%
ValueCountFrequency (%)
51 1
 
< 0.1%
44 2
< 0.1%
41 1
 
< 0.1%
37 1
 
< 0.1%
31 2
< 0.1%
29 2
< 0.1%
28 2
< 0.1%
27 3
< 0.1%
25 2
< 0.1%
24 2
< 0.1%

nb_of_photos
Real number (ℝ)

High correlation  Zeros 

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.67588775
Minimum0
Maximum19
Zeros54290
Zeros (%)83.2%
Negative0
Negative (%)0.0%
Memory size764.6 KiB
2025-03-21T08:10:55.901866image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5
Maximum19
Range19
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.875916
Coefficient of variation (CV)2.7754845
Kurtosis11.741421
Mean0.67588775
Median Absolute Deviation (MAD)0
Skewness3.3221925
Sum44101
Variance3.5190607
MonotonicityNot monotonic
2025-03-21T08:10:56.061487image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 54290
83.2%
1 2204
 
3.4%
2 1906
 
2.9%
3 1532
 
2.3%
4 1248
 
1.9%
5 1081
 
1.7%
6 1047
 
1.6%
7 601
 
0.9%
8 463
 
0.7%
9 386
 
0.6%
Other values (9) 491
 
0.8%
ValueCountFrequency (%)
0 54290
83.2%
1 2204
 
3.4%
2 1906
 
2.9%
3 1532
 
2.3%
4 1248
 
1.9%
5 1081
 
1.7%
6 1047
 
1.6%
7 601
 
0.9%
8 463
 
0.7%
9 386
 
0.6%
ValueCountFrequency (%)
19 1
 
< 0.1%
18 2
 
< 0.1%
17 2
 
< 0.1%
15 6
 
< 0.1%
14 13
 
< 0.1%
13 19
 
< 0.1%
12 99
 
0.2%
11 139
 
0.2%
10 210
0.3%
9 386
0.6%

nb_repeat_sitters
Real number (ℝ)

High correlation  Zeros 

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4888504
Minimum0
Maximum24
Zeros46198
Zeros (%)70.8%
Negative0
Negative (%)0.0%
Memory size764.6 KiB
2025-03-21T08:10:56.213402image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum24
Range24
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.004329
Coefficient of variation (CV)2.054471
Kurtosis25.651952
Mean0.4888504
Median Absolute Deviation (MAD)0
Skewness3.6771493
Sum31897
Variance1.0086767
MonotonicityNot monotonic
2025-03-21T08:10:56.372462image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 46198
70.8%
1 11921
 
18.3%
2 4149
 
6.4%
3 1640
 
2.5%
4 711
 
1.1%
5 312
 
0.5%
6 140
 
0.2%
7 82
 
0.1%
8 43
 
0.1%
9 18
 
< 0.1%
Other values (10) 35
 
0.1%
ValueCountFrequency (%)
0 46198
70.8%
1 11921
 
18.3%
2 4149
 
6.4%
3 1640
 
2.5%
4 711
 
1.1%
5 312
 
0.5%
6 140
 
0.2%
7 82
 
0.1%
8 43
 
0.1%
9 18
 
< 0.1%
ValueCountFrequency (%)
24 1
 
< 0.1%
18 1
 
< 0.1%
17 1
 
< 0.1%
16 1
 
< 0.1%
15 1
 
< 0.1%
14 5
< 0.1%
13 5
< 0.1%
12 1
 
< 0.1%
11 7
< 0.1%
10 12
< 0.1%

nb_unique_sitters
Real number (ℝ)

High correlation  Zeros 

Distinct72
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9441524
Minimum0
Maximum139
Zeros12714
Zeros (%)19.5%
Negative0
Negative (%)0.0%
Memory size764.6 KiB
2025-03-21T08:10:56.529988image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q37
95-th percentile17
Maximum139
Range139
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.1837991
Coefficient of variation (CV)1.2507299
Kurtosis15.519098
Mean4.9441524
Median Absolute Deviation (MAD)3
Skewness2.7727375
Sum322601
Variance38.239371
MonotonicityNot monotonic
2025-03-21T08:10:56.711010image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12714
19.5%
1 11216
17.2%
2 6926
10.6%
3 5175
7.9%
4 4607
 
7.1%
5 3803
 
5.8%
6 3126
 
4.8%
7 2795
 
4.3%
8 2304
 
3.5%
9 1895
 
2.9%
Other values (62) 10688
16.4%
ValueCountFrequency (%)
0 12714
19.5%
1 11216
17.2%
2 6926
10.6%
3 5175
7.9%
4 4607
 
7.1%
5 3803
 
5.8%
6 3126
 
4.8%
7 2795
 
4.3%
8 2304
 
3.5%
9 1895
 
2.9%
ValueCountFrequency (%)
139 1
< 0.1%
100 1
< 0.1%
96 1
< 0.1%
88 1
< 0.1%
80 1
< 0.1%
76 2
< 0.1%
74 1
< 0.1%
70 1
< 0.1%
64 2
< 0.1%
63 2
< 0.1%

pets_welcome
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size573.5 KiB
True
50547 
False
14702 
ValueCountFrequency (%)
True 50547
77.5%
False 14702
 
22.5%
2025-03-21T08:10:56.870684image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

wish_to_meet_in_person
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size573.5 KiB
True
64011 
False
 
1238
ValueCountFrequency (%)
True 64011
98.1%
False 1238
 
1.9%
2025-03-21T08:10:57.069109image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

wish_to_video_call
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size573.5 KiB
True
64866 
False
 
383
ValueCountFrequency (%)
True 64866
99.4%
False 383
 
0.6%
2025-03-21T08:10:57.173458image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

other_animals
Categorical

High cardinality  Imbalance 

Distinct330
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size647.9 KiB
None
64813 
bees
 
14
hamster
 
10
tortoise
 
10
rabbits
 
9
Other values (325)
 
393

Length

Max length601
Median length4
Mean length4.1167527
Min length1

Characters and Unicode

Total characters268614
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique292 ?
Unique (%)0.4%

Sample

1st rowNone
2nd rowNone
3rd rowbirds
4th rowNone
5th rowNone

Common Values

ValueCountFrequency (%)
None 64813
99.3%
bees 14
 
< 0.1%
hamster 10
 
< 0.1%
tortoise 10
 
< 0.1%
rabbits 9
 
< 0.1%
guinea pigs 8
 
< 0.1%
plants 7
 
< 0.1%
rabbit 5
 
< 0.1%
guinea pig 5
 
< 0.1%
alpacas 4
 
< 0.1%
Other values (320) 364
 
0.6%

Length

2025-03-21T08:10:57.361185image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
none 64816
97.4%
and 64
 
0.1%
a 37
 
0.1%
2 37
 
0.1%
31
 
< 0.1%
the 27
 
< 0.1%
to 26
 
< 0.1%
wild 26
 
< 0.1%
guinea 24
 
< 0.1%
bees 24
 
< 0.1%
Other values (573) 1456
 
2.2%

Most occurring characters

ValueCountFrequency (%)
e 65750
24.5%
o 65373
24.3%
n 65256
24.3%
N 64813
24.1%
1358
 
0.5%
a 652
 
0.2%
s 622
 
0.2%
t 572
 
0.2%
i 525
 
0.2%
r 492
 
0.2%
Other values (39) 3201
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 268614
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 65750
24.5%
o 65373
24.3%
n 65256
24.3%
N 64813
24.1%
1358
 
0.5%
a 652
 
0.2%
s 622
 
0.2%
t 572
 
0.2%
i 525
 
0.2%
r 492
 
0.2%
Other values (39) 3201
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 268614
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 65750
24.5%
o 65373
24.3%
n 65256
24.3%
N 64813
24.1%
1358
 
0.5%
a 652
 
0.2%
s 622
 
0.2%
t 572
 
0.2%
i 525
 
0.2%
r 492
 
0.2%
Other values (39) 3201
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 268614
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 65750
24.5%
o 65373
24.3%
n 65256
24.3%
N 64813
24.1%
1358
 
0.5%
a 652
 
0.2%
s 622
 
0.2%
t 572
 
0.2%
i 525
 
0.2%
r 492
 
0.2%
Other values (39) 3201
 
1.2%

days_since_modified
Real number (ℝ)

High correlation 

Distinct1398
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean624.46094
Minimum52
Maximum2967
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size700.9 KiB
2025-03-21T08:10:57.532714image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum52
5-th percentile78
Q1276
median663
Q3956
95-th percentile1110
Maximum2967
Range2915
Interquartile range (IQR)680

Descriptive statistics

Standard deviation361.68629
Coefficient of variation (CV)0.57919761
Kurtosis-1.0352025
Mean624.46094
Median Absolute Deviation (MAD)329
Skewness-0.033958234
Sum40745452
Variance130816.97
MonotonicityNot monotonic
2025-03-21T08:10:57.722745image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52 169
 
0.3%
67 156
 
0.2%
60 148
 
0.2%
53 144
 
0.2%
1017 144
 
0.2%
73 144
 
0.2%
59 143
 
0.2%
54 143
 
0.2%
977 140
 
0.2%
74 139
 
0.2%
Other values (1388) 63779
97.7%
ValueCountFrequency (%)
52 169
0.3%
53 144
0.2%
54 143
0.2%
55 91
0.1%
56 124
0.2%
57 119
0.2%
58 121
0.2%
59 143
0.2%
60 148
0.2%
61 129
0.2%
ValueCountFrequency (%)
2967 1
< 0.1%
2907 1
< 0.1%
2887 1
< 0.1%
2813 1
< 0.1%
2601 1
< 0.1%
2560 1
< 0.1%
2541 1
< 0.1%
2526 1
< 0.1%
2497 1
< 0.1%
2490 1
< 0.1%

year_approved
Real number (ℝ)

High correlation 

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2021.0827
Minimum2012
Maximum2025
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size700.9 KiB
2025-03-21T08:10:57.895978image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2012
5-th percentile2017
Q12021
median2022
Q32022
95-th percentile2022
Maximum2025
Range13
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.8269667
Coefficient of variation (CV)0.00090395448
Kurtosis2.2902404
Mean2021.0827
Median Absolute Deviation (MAD)0
Skewness-1.7948321
Sum1.3187362 × 108
Variance3.3378074
MonotonicityNot monotonic
2025-03-21T08:10:58.046555image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2022 45641
69.9%
2021 4897
 
7.5%
2019 4200
 
6.4%
2018 3255
 
5.0%
2017 2324
 
3.6%
2016 2176
 
3.3%
2023 960
 
1.5%
2020 821
 
1.3%
2015 408
 
0.6%
2024 320
 
0.5%
Other values (4) 247
 
0.4%
ValueCountFrequency (%)
2012 24
 
< 0.1%
2013 49
 
0.1%
2014 146
 
0.2%
2015 408
 
0.6%
2016 2176
3.3%
2017 2324
3.6%
2018 3255
5.0%
2019 4200
6.4%
2020 821
 
1.3%
2021 4897
7.5%
ValueCountFrequency (%)
2025 28
 
< 0.1%
2024 320
 
0.5%
2023 960
 
1.5%
2022 45641
69.9%
2021 4897
 
7.5%
2020 821
 
1.3%
2019 4200
 
6.4%
2018 3255
 
5.0%
2017 2324
 
3.6%
2016 2176
 
3.3%

attraction_city
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size573.5 KiB
True
37106 
False
28143 
ValueCountFrequency (%)
True 37106
56.9%
False 28143
43.1%
2025-03-21T08:10:58.172803image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size573.5 KiB
False
49620 
True
15629 
ValueCountFrequency (%)
False 49620
76.0%
True 15629
 
24.0%
2025-03-21T08:10:58.283693image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size573.5 KiB
False
53149 
True
12100 
ValueCountFrequency (%)
False 53149
81.5%
True 12100
 
18.5%
2025-03-21T08:10:58.391846image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

attraction_countryside
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size573.5 KiB
False
34298 
True
30951 
ValueCountFrequency (%)
False 34298
52.6%
True 30951
47.4%
2025-03-21T08:10:58.501225image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

pet_dog
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size573.5 KiB
True
46286 
False
18963 
ValueCountFrequency (%)
True 46286
70.9%
False 18963
29.1%
2025-03-21T08:10:58.610552image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

pet_cat
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size573.5 KiB
False
34660 
True
30589 
ValueCountFrequency (%)
False 34660
53.1%
True 30589
46.9%
2025-03-21T08:10:58.729540image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

pet_bird
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size573.5 KiB
False
64298 
True
 
951
ValueCountFrequency (%)
False 64298
98.5%
True 951
 
1.5%
2025-03-21T08:10:58.850354image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

pet_fish
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size573.5 KiB
False
62146 
True
 
3103
ValueCountFrequency (%)
False 62146
95.2%
True 3103
 
4.8%
2025-03-21T08:10:58.964841image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

pet_reptile
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size573.5 KiB
False
64254 
True
 
995
ValueCountFrequency (%)
False 64254
98.5%
True 995
 
1.5%
2025-03-21T08:10:59.092870image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

pet_poultry
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size573.5 KiB
False
61346 
True
 
3903
ValueCountFrequency (%)
False 61346
94.0%
True 3903
 
6.0%
2025-03-21T08:10:59.228598image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

pet_farm_animal
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size573.5 KiB
False
64195 
True
 
1054
ValueCountFrequency (%)
False 64195
98.4%
True 1054
 
1.6%
2025-03-21T08:10:59.359525image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

photo_interior
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size573.5 KiB
False
61512 
True
 
3737
ValueCountFrequency (%)
False 61512
94.3%
True 3737
 
5.7%
2025-03-21T08:10:59.495931image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

photo_exterior
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size573.5 KiB
False
61432 
True
 
3817
ValueCountFrequency (%)
False 61432
94.2%
True 3817
 
5.8%
2025-03-21T08:10:59.634249image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

photo_attraction
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size573.5 KiB
False
64780 
True
 
469
ValueCountFrequency (%)
False 64780
99.3%
True 469
 
0.7%
2025-03-21T08:10:59.774456image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

photo_garden
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size573.5 KiB
False
61432 
True
 
3817
ValueCountFrequency (%)
False 61432
94.2%
True 3817
 
5.8%
2025-03-21T08:10:59.897002image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

photo_pool
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size573.5 KiB
False
64974 
True
 
275
ValueCountFrequency (%)
False 64974
99.6%
True 275
 
0.4%
2025-03-21T08:11:00.015255image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

photo_view
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size573.5 KiB
False
64830 
True
 
419
ValueCountFrequency (%)
False 64830
99.4%
True 419
 
0.6%
2025-03-21T08:11:00.133387image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

welcomes_single
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size573.5 KiB
True
64031 
False
 
1218
ValueCountFrequency (%)
True 64031
98.1%
False 1218
 
1.9%
2025-03-21T08:11:00.245472image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

welcomes_couple
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size573.5 KiB
True
64023 
False
 
1226
ValueCountFrequency (%)
True 64023
98.1%
False 1226
 
1.9%
2025-03-21T08:11:00.369804image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

welcomes_family
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size573.5 KiB
False
46131 
True
19118 
ValueCountFrequency (%)
False 46131
70.7%
True 19118
29.3%
2025-03-21T08:11:00.489248image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

welcomes_any_age
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size573.5 KiB
True
59703 
False
 
5546
ValueCountFrequency (%)
True 59703
91.5%
False 5546
 
8.5%
2025-03-21T08:11:00.611951image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

welcomes_baby
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size573.5 KiB
False
62176 
True
 
3073
ValueCountFrequency (%)
False 62176
95.3%
True 3073
 
4.7%
2025-03-21T08:11:00.735323image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

welcomes_toddler
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size573.5 KiB
False
61306 
True
 
3943
ValueCountFrequency (%)
False 61306
94.0%
True 3943
 
6.0%
2025-03-21T08:11:00.854663image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

welcomes_child
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size573.5 KiB
False
60300 
True
 
4949
ValueCountFrequency (%)
False 60300
92.4%
True 4949
 
7.6%
2025-03-21T08:11:00.969755image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

welcomes_teen
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size573.5 KiB
False
60348 
True
 
4901
ValueCountFrequency (%)
False 60348
92.5%
True 4901
 
7.5%
2025-03-21T08:11:01.090397image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

welcomes_young
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size573.5 KiB
False
60771 
True
 
4478
ValueCountFrequency (%)
False 60771
93.1%
True 4478
 
6.9%
2025-03-21T08:11:01.203183image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Interactions

2025-03-21T08:10:48.267690image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:28.932076image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:30.783393image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:32.507641image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:34.260754image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:35.933735image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:37.511776image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:39.317169image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:40.960665image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:42.754203image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:44.688035image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:46.413651image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:48.415293image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:29.079238image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:30.927434image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:32.649022image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:34.388603image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:36.070226image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:37.638075image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:39.459772image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:41.124794image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:42.904852image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:44.824407image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:46.584833image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:48.575024image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:29.317918image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:31.078400image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:32.805872image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:34.527869image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:36.212098image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:37.799190image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:39.619014image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:41.295757image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:43.058576image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:44.979796image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:46.729481image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:48.717875image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:29.466416image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:31.219047image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:32.944350image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:34.667414image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:36.346382image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:37.950438image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:39.758162image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:41.446992image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:43.229738image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:45.127300image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:46.887069image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:48.858328image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:29.604451image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:31.363798image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:33.076155image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:34.818519image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:36.478019image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:38.108348image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:39.886109image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:41.590578image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:43.370189image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:45.277657image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:47.070579image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:48.994574image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:29.739208image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:31.497304image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:33.203119image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:34.948148image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:36.614275image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:38.246757image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:40.020084image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:41.715744image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:43.514931image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:45.422011image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:47.217210image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:49.125236image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:29.877354image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:31.627967image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:33.346772image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:35.088162image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:36.744939image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:38.370479image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:40.149432image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:41.874521image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:43.667113image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:45.561606image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:47.354583image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:49.248374image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:30.024329image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:31.754628image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:33.487488image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:35.224023image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:36.882548image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:38.501003image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:40.279907image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:42.026162image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:43.819924image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:45.703684image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:47.510076image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:49.478907image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:30.200102image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:31.895552image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:33.644102image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:35.360746image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:37.016330image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:38.651110image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:40.410254image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:42.157974image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:43.980980image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:45.843142image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:47.713299image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:49.609349image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:30.351679image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:32.036260image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:33.773335image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:35.495488image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:37.134251image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:38.776836image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:40.529336image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:42.292607image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:44.218510image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:45.962135image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:47.844177image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:49.742239image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:30.491699image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:32.195308image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:33.906025image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:35.643814image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:37.258294image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:39.018354image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:40.688514image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:42.436084image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:44.377418image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:46.099123image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:47.984468image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:49.875505image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:30.636969image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:32.341981image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:34.127029image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:35.788008image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:37.387497image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:39.155814image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:40.821862image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:42.599807image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:44.538880image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:46.260053image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-21T08:10:48.119846image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2025-03-21T08:11:01.475499image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
accessible_by_public_transportattraction_beachattraction_cityattraction_countrysideattraction_mountainavg_nb_apps_per_assgcar_includedcar_requireddays_since_modifieddisabled_accessfamily_friendlyhome_typeidminutes_pet_can_be_left_alonenb_assignments_fillednb_assignments_publishednb_distinct_petsnb_domestic_sittersnb_invitesnb_of_petsnb_of_photosnb_repeat_sittersnb_unique_sitterspet_birdpet_catpet_dogpet_farm_animalpet_fishpet_poultrypet_reptilepets_welcomephoto_attractionphoto_exteriorphoto_gardenphoto_interiorphoto_poolphoto_viewwelcomes_any_agewelcomes_babywelcomes_childwelcomes_couplewelcomes_familywelcomes_singlewelcomes_teenwelcomes_toddlerwelcomes_youngwish_to_meet_in_personwish_to_video_callyear_approved
accessible_by_public_transport1.0000.0210.4120.3130.1690.0240.0250.8410.0960.0320.0110.3180.0220.0950.0570.0530.1600.0090.0020.1010.0000.0160.0620.0330.0720.1600.1040.0300.1490.0170.0930.0050.0420.0420.0000.0290.0190.0310.0260.0290.0610.0110.0200.0270.0280.0270.0200.0060.021
attraction_beach0.0211.0000.1580.0920.0990.0000.0400.0340.0380.0330.0330.0150.0610.0360.0000.0070.0200.0140.0030.0000.0440.0130.0070.0180.0270.0390.0190.0090.0200.0040.0170.0410.0270.0270.0220.0240.0210.0130.0130.0130.0030.0330.0220.0110.0120.0110.0050.0040.056
attraction_city0.4120.1581.0000.5560.1950.0210.0500.4030.0420.0330.0040.2980.0660.0910.0490.0470.1810.0390.0040.1150.0060.0240.0520.0280.0610.1530.1180.0440.1800.0060.0400.0120.0420.0420.0080.0150.0310.0110.0170.0080.0510.0040.0520.0080.0120.0090.0000.0000.047
attraction_countryside0.3130.0920.5561.0000.0230.0450.0340.3130.0630.0410.0190.3320.1370.0910.0210.0160.1950.0360.0000.1190.0840.0120.0230.0260.0370.1250.1200.0610.1880.0180.0360.0240.0970.0970.0550.0170.0390.0000.0110.0000.0540.0190.0510.0000.0020.0000.0000.0060.102
attraction_mountain0.1690.0990.1950.0231.0000.0240.0660.1720.0650.0040.0090.0870.0560.0470.0210.0280.0420.0360.0000.0290.0430.0160.0240.0090.0000.0330.0220.0040.0330.0000.0350.0140.0430.0430.0350.0190.0320.0290.0250.0290.0050.0090.0240.0280.0250.0260.0100.0110.052
avg_nb_apps_per_assg0.0240.0000.0210.0450.0241.0000.0080.0480.1750.0000.0800.0300.1390.1170.1650.1760.0170.1520.0130.0200.1040.1380.1460.0090.0170.0000.0100.0040.0030.0000.1160.0270.0600.0600.0640.0140.0260.0300.0200.0270.0410.0800.0400.0280.0250.0270.0260.0140.139
car_included0.0250.0400.0500.0340.0660.0081.0000.0300.0290.0230.0690.0740.0340.0390.0250.0230.0600.0260.0000.0520.0170.0150.0250.0090.0190.0750.0150.0080.0230.0070.0110.0090.0210.0210.0120.0160.0060.0540.0390.0530.0200.0690.0420.0520.0430.0530.0090.0000.034
car_required0.8410.0340.4030.3130.1720.0480.0301.0000.0210.0150.0000.3280.0530.0600.0360.0310.1620.0090.0000.0970.0240.0000.0410.0340.0690.1590.1020.0330.1510.0170.0270.0000.0570.0570.0130.0290.0240.0000.0060.0000.0460.0000.0390.0000.0000.0000.0110.0040.032
days_since_modified0.0960.0380.0420.0630.0650.1750.0290.0211.0000.0070.1000.0450.0320.336-0.635-0.635-0.003-0.567-0.306-0.028-0.121-0.363-0.6380.0170.0910.0880.0180.0230.0220.0140.6120.0350.0940.0940.0910.0280.0310.3240.2360.3060.1550.1000.1550.3050.2690.2900.1570.0850.109
disabled_access0.0320.0330.0330.0410.0040.0000.0230.0150.0071.0000.0610.1720.0280.0080.0060.0120.0330.0070.0080.0140.0260.0010.0120.0100.0090.0060.0100.0120.0290.0000.0070.0080.0270.0270.0250.0020.0060.0190.0170.0170.0050.0610.0080.0160.0160.0130.0000.0070.026
family_friendly0.0110.0330.0040.0190.0090.0800.0690.0000.1000.0611.0000.0790.0620.0300.0490.0460.1040.0410.0000.0380.0200.0510.0400.0050.0300.1080.0210.0380.0470.0420.1270.0040.0120.0120.0170.0000.0060.4730.3450.4450.0611.0000.0220.4420.3940.4220.0260.0120.052
home_type0.3180.0150.2980.3320.0870.0300.0740.3280.0450.1720.0791.0000.0670.0610.0330.0300.1340.0120.0000.0550.0430.0140.0360.0300.1000.2290.0580.0760.1180.0360.0490.0110.0820.0820.0470.0190.0150.0500.0310.0480.0760.0790.0370.0480.0400.0410.0020.0040.056
id0.0220.0610.0660.1370.0560.1390.0340.0530.0320.0280.0620.0671.0000.058-0.361-0.391-0.050-0.288-0.202-0.067-0.538-0.244-0.3590.0120.0750.0590.0420.0320.0610.0090.1260.1480.4360.4360.4350.1130.1390.0340.0290.0310.0220.0620.0870.0310.0300.0280.0240.0250.710
minutes_pet_can_be_left_alone0.0950.0360.0910.0910.0470.1170.0390.0600.3360.0080.0300.0610.0581.0000.0930.0960.0310.0830.0060.0060.0410.0570.0940.0130.1980.2660.0110.0100.0180.0090.5890.0180.0530.0530.0520.0100.0210.4430.3320.4200.2010.0300.2130.4200.3740.4030.2210.1260.077
nb_assignments_filled0.0570.0000.0490.0210.0210.1650.0250.036-0.6350.0060.0490.033-0.3610.0931.0000.952-0.0390.8900.447-0.0180.3770.6450.9870.0090.0710.0860.0000.0080.0050.0090.1790.0650.1310.1310.1490.0330.0460.0770.0650.0730.0500.0490.0570.0720.0720.0720.0870.060-0.426
nb_assignments_published0.0530.0070.0470.0160.0280.1760.0230.031-0.6350.0120.0460.030-0.3910.0960.9521.000-0.0200.8480.4970.0070.3890.6290.9380.0100.0690.0790.0090.0000.0020.0040.1810.0660.1420.1420.1560.0530.0490.0850.0680.0780.0490.0460.0550.0790.0760.0800.0870.064-0.444
nb_distinct_pets0.1600.0200.1810.1950.0420.0170.0600.162-0.0030.0330.1040.134-0.0500.031-0.039-0.0201.000-0.0420.0200.6940.055-0.040-0.0340.2680.3830.3000.4890.4340.5980.3240.0230.0070.0670.0670.0400.0240.0280.0650.0500.0650.0440.1040.0300.0670.0560.0600.0060.017-0.033
nb_domestic_sitters0.0090.0140.0390.0360.0360.1520.0260.009-0.5670.0070.0410.012-0.2880.0830.8900.848-0.0421.0000.427-0.0340.3070.6090.8710.0000.0530.0670.0000.0000.0060.0000.1460.0450.1170.1170.1300.0280.0360.0740.0590.0680.0380.0410.0240.0690.0660.0700.0690.051-0.348
nb_invites0.0020.0030.0040.0000.0000.0130.0000.000-0.3060.0080.0000.000-0.2020.0060.4470.4970.0200.4271.0000.0490.1860.3960.4200.0000.0000.0070.0000.0000.0000.0070.0030.0030.0040.0040.0080.0000.0000.0120.0180.0130.0140.0000.0000.0130.0150.0140.0000.000-0.214
nb_of_pets0.1010.0000.1150.1190.0290.0200.0520.097-0.0280.0140.0380.055-0.0670.006-0.0180.0070.694-0.0340.0491.0000.071-0.021-0.0140.2150.1330.0710.2550.1410.2820.1860.0150.0000.0220.0220.0000.0170.0000.0230.0160.0230.0150.0380.0400.0230.0230.0230.0000.000-0.050
nb_of_photos0.0000.0440.0060.0840.0430.1040.0170.024-0.1210.0260.0200.043-0.5380.0410.3770.3890.0550.3070.1860.0711.0000.2480.3770.0040.0750.0430.0370.0240.0560.0000.0830.2420.6530.6530.7190.1830.2320.0260.0160.0210.0000.0200.0640.0210.0190.0210.0190.021-0.652
nb_repeat_sitters0.0160.0130.0240.0120.0160.1380.0150.000-0.3630.0010.0510.014-0.2440.0570.6450.629-0.0400.6090.396-0.0210.2481.0000.5530.0000.0460.0650.0000.0000.0000.0000.1200.0400.0960.0960.1020.0320.0300.0300.0180.0260.0450.0510.0390.0270.0250.0260.0320.018-0.277
nb_unique_sitters0.0620.0070.0520.0230.0240.1460.0250.041-0.6380.0120.0400.036-0.3590.0940.9870.938-0.0340.8710.420-0.0140.3770.5531.0000.0090.0690.0750.0090.0070.0000.0000.1740.0770.1270.1270.1440.0260.0400.0850.0710.0790.0510.0400.0550.0810.0770.0800.0980.066-0.426
pet_bird0.0330.0180.0280.0260.0090.0090.0090.0340.0170.0100.0050.0300.0120.0130.0090.0100.2680.0000.0000.2150.0040.0000.0091.0000.0090.0060.0330.0700.0690.0700.0130.0030.0090.0090.0050.0060.0000.0000.0000.0000.0120.0050.0000.0000.0000.0020.0030.0000.011
pet_cat0.0720.0270.0610.0370.0000.0170.0190.0690.0910.0090.0300.1000.0750.1980.0710.0690.3830.0530.0000.1330.0750.0460.0690.0091.0000.6110.0290.0220.0370.0200.1080.0150.0220.0220.0440.0000.0080.0120.0180.0100.0520.0300.0140.0110.0100.0130.0290.0100.076
pet_dog0.1600.0390.1530.1250.0330.0000.0750.1590.0880.0060.1080.2290.0590.2660.0860.0790.3000.0670.0070.0710.0430.0650.0750.0060.6111.0000.0350.0340.0620.0260.1160.0090.0050.0050.0390.0080.0000.0420.0210.0420.0860.1080.0280.0430.0340.0380.0230.0120.063
pet_farm_animal0.1040.0190.1180.1200.0220.0100.0150.1020.0180.0100.0210.0580.0420.0110.0000.0090.4890.0000.0000.2550.0370.0000.0090.0330.0290.0351.0000.0300.3170.0220.0000.0000.0380.0380.0200.0070.0140.0260.0220.0260.0100.0210.0240.0260.0270.0230.0000.0000.038
pet_fish0.0300.0090.0440.0610.0040.0040.0080.0330.0230.0120.0380.0760.0320.0100.0080.0000.4340.0000.0000.1410.0240.0000.0070.0700.0220.0340.0301.0000.1000.0970.0140.0000.0180.0180.0150.0090.0000.0120.0140.0150.0150.0380.0010.0130.0170.0090.0030.0000.022
pet_poultry0.1490.0200.1800.1880.0330.0030.0230.1510.0220.0290.0470.1180.0610.0180.0050.0020.5980.0060.0000.2820.0560.0000.0000.0690.0370.0620.3170.1001.0000.0630.0040.0050.0690.0690.0300.0220.0210.0340.0250.0340.0230.0470.0360.0330.0300.0310.0020.0060.052
pet_reptile0.0170.0040.0060.0180.0000.0000.0070.0170.0140.0000.0420.0360.0090.0090.0090.0040.3240.0000.0070.1860.0000.0000.0000.0700.0200.0260.0220.0970.0631.0000.0140.0000.0000.0000.0050.0000.0000.0200.0130.0220.0080.0420.0030.0210.0160.0200.0000.0050.015
pets_welcome0.0930.0170.0400.0360.0350.1160.0110.0270.6120.0070.1270.0490.1260.5890.1790.1810.0230.1460.0030.0150.0830.1200.1740.0130.1080.1160.0000.0140.0040.0141.0000.0150.0440.0440.0470.0070.0260.1620.1070.1520.1610.1270.1500.1550.1270.1480.1490.0710.163
photo_attraction0.0050.0410.0120.0240.0140.0270.0090.0000.0350.0080.0040.0110.1480.0180.0650.0660.0070.0450.0030.0000.2420.0400.0770.0030.0150.0090.0000.0000.0050.0000.0151.0000.1550.1550.1550.0660.1670.0000.0000.0000.0000.0040.0180.0020.0000.0020.0110.0050.155
photo_exterior0.0420.0270.0420.0970.0430.0600.0210.0570.0940.0270.0120.0820.4360.0530.1310.1420.0670.1170.0040.0220.6530.0960.1270.0090.0220.0050.0380.0180.0690.0000.0440.1551.0001.0000.5850.1790.1700.0190.0030.0170.0030.0120.0610.0150.0130.0140.0110.0130.469
photo_garden0.0420.0270.0420.0970.0430.0600.0210.0570.0940.0270.0120.0820.4360.0530.1310.1420.0670.1170.0040.0220.6530.0960.1270.0090.0220.0050.0380.0180.0690.0000.0440.1551.0001.0000.5850.1790.1700.0190.0030.0170.0030.0120.0610.0150.0130.0140.0110.0130.469
photo_interior0.0000.0220.0080.0550.0350.0640.0120.0130.0910.0250.0170.0470.4350.0520.1490.1560.0400.1300.0080.0000.7190.1020.1440.0050.0440.0390.0200.0150.0300.0050.0470.1550.5850.5851.0000.1430.1690.0130.0000.0110.0000.0170.0360.0120.0070.0100.0080.0110.476
photo_pool0.0290.0240.0150.0170.0190.0140.0160.0290.0280.0020.0000.0190.1130.0100.0330.0530.0240.0280.0000.0170.1830.0320.0260.0060.0000.0080.0070.0090.0220.0000.0070.0660.1790.1790.1431.0000.0730.0000.0000.0000.0000.0000.0210.0000.0000.0000.0100.0000.121
photo_view0.0190.0210.0310.0390.0320.0260.0060.0240.0310.0060.0060.0150.1390.0210.0460.0490.0280.0360.0000.0000.2320.0300.0400.0000.0080.0000.0140.0000.0210.0000.0260.1670.1700.1700.1690.0731.0000.0000.0050.0000.0000.0060.0250.0000.0000.0000.0140.0000.152
welcomes_any_age0.0310.0130.0110.0000.0290.0300.0540.0000.3240.0190.4730.0500.0340.4430.0770.0850.0650.0740.0120.0230.0260.0300.0850.0000.0120.0420.0260.0120.0340.0200.1620.0000.0190.0190.0130.0000.0001.0000.7290.9400.0000.4730.0600.9350.8320.8910.1440.0750.049
welcomes_baby0.0260.0130.0170.0110.0250.0200.0390.0060.2360.0170.3450.0310.0290.3320.0650.0680.0500.0590.0180.0160.0160.0180.0710.0000.0180.0210.0220.0140.0250.0130.1070.0000.0030.0030.0000.0000.0050.7291.0000.7350.0150.3450.0130.7010.8480.6660.1270.0700.038
welcomes_child0.0290.0130.0080.0000.0290.0270.0530.0000.3060.0170.4450.0480.0310.4200.0730.0780.0650.0680.0130.0230.0210.0260.0790.0000.0100.0420.0260.0150.0340.0220.1520.0000.0170.0170.0110.0000.0000.9400.7351.0000.0120.4450.0470.9320.8660.8560.1410.0770.043
welcomes_couple0.0610.0030.0510.0540.0050.0410.0200.0460.1550.0050.0610.0760.0220.2010.0500.0490.0440.0380.0140.0150.0000.0450.0510.0120.0520.0860.0100.0150.0230.0080.1610.0000.0030.0030.0000.0000.0000.0000.0150.0121.0000.0610.0430.0120.0170.0050.0260.0340.036
welcomes_family0.0110.0330.0040.0190.0090.0800.0690.0000.1000.0611.0000.0790.0620.0300.0490.0460.1040.0410.0000.0380.0200.0510.0400.0050.0300.1080.0210.0380.0470.0420.1270.0040.0120.0120.0170.0000.0060.4730.3450.4450.0611.0000.0220.4420.3940.4220.0260.0120.052
welcomes_single0.0200.0220.0520.0510.0240.0400.0420.0390.1550.0080.0220.0370.0870.2130.0570.0550.0300.0240.0000.0400.0640.0390.0550.0000.0140.0280.0240.0010.0360.0030.1500.0180.0610.0610.0360.0210.0250.0600.0130.0470.0430.0221.0000.0480.0280.0410.0540.0240.088
welcomes_teen0.0270.0110.0080.0000.0280.0280.0520.0000.3050.0160.4420.0480.0310.4200.0720.0790.0670.0690.0130.0230.0210.0270.0810.0000.0110.0430.0260.0130.0330.0210.1550.0020.0150.0150.0120.0000.0000.9350.7010.9320.0120.4420.0481.0000.8080.9120.1450.0760.045
welcomes_toddler0.0280.0120.0120.0020.0250.0250.0430.0000.2690.0160.3940.0400.0300.3740.0720.0760.0560.0660.0150.0230.0190.0250.0770.0000.0100.0340.0270.0170.0300.0160.1270.0000.0130.0130.0070.0000.0000.8320.8480.8660.0170.3940.0280.8081.0000.7490.1310.0730.041
welcomes_young0.0270.0110.0090.0000.0260.0270.0530.0000.2900.0130.4220.0410.0280.4030.0720.0800.0600.0700.0140.0230.0210.0260.0800.0020.0130.0380.0230.0090.0310.0200.1480.0020.0140.0140.0100.0000.0000.8910.6660.8560.0050.4220.0410.9120.7491.0000.1440.0780.042
wish_to_meet_in_person0.0200.0050.0000.0000.0100.0260.0090.0110.1570.0000.0260.0020.0240.2210.0870.0870.0060.0690.0000.0000.0190.0320.0980.0030.0290.0230.0000.0030.0020.0000.1490.0110.0110.0110.0080.0100.0140.1440.1270.1410.0260.0260.0540.1450.1310.1441.0000.2560.032
wish_to_video_call0.0060.0040.0000.0060.0110.0140.0000.0040.0850.0070.0120.0040.0250.1260.0600.0640.0170.0510.0000.0000.0210.0180.0660.0000.0100.0120.0000.0000.0060.0050.0710.0050.0130.0130.0110.0000.0000.0750.0700.0770.0340.0120.0240.0760.0730.0780.2561.0000.031
year_approved0.0210.0560.0470.1020.0520.1390.0340.0320.1090.0260.0520.0560.7100.077-0.426-0.444-0.033-0.348-0.214-0.050-0.652-0.277-0.4260.0110.0760.0630.0380.0220.0520.0150.1630.1550.4690.4690.4760.1210.1520.0490.0380.0430.0360.0520.0880.0450.0410.0420.0320.0311.000

Missing values

2025-03-21T08:10:50.158284image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-21T08:10:51.040748image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

accessible_by_public_transportavg_nb_apps_per_assgcar_includedcar_requireddisabled_accessfamily_friendlyhome_typeidminutes_pet_can_be_left_alonenb_assignments_fillednb_assignments_publishednb_distinct_petsnb_domestic_sittersnb_invitesnb_of_petsnb_of_photosnb_repeat_sittersnb_unique_sitterspets_welcomewish_to_meet_in_personwish_to_video_callother_animalsdays_since_modifiedyear_approvedattraction_cityattraction_beachattraction_mountainattraction_countrysidepet_dogpet_catpet_birdpet_fishpet_reptilepet_poultrypet_farm_animalphoto_interiorphoto_exteriorphoto_attractionphoto_gardenphoto_poolphoto_viewwelcomes_singlewelcomes_couplewelcomes_familywelcomes_any_agewelcomes_babywelcomes_toddlerwelcomes_childwelcomes_teenwelcomes_young
0True0FalseFalseFalseFalseapartment20136014191105210113FalseTrueTrueNone4902016TrueFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueTrueFalseTrueFalseFalseFalseFalseFalse
1True0FalseTrueTrueFalsehouse164632403142212203FalseTrueTrueNone572015FalseFalseFalseTrueTrueTrueFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseTrueTrueFalseTrueFalseFalseFalseFalseFalse
2False0FalseTrueTrueFalseapartment7761201521235040015TrueTrueTruebirds1582012FalseTrueFalseTrueTrueTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueTrueFalseTrueFalseFalseFalseFalseFalse
3True0FalseFalseFalseFalsehouse1033240111219240011FalseTrueTrueNone1292022TrueFalseFalseTrueFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueTrueFalseTrueFalseFalseFalseFalseFalse
4True0TrueFalseTrueFalsehouse1325240672323114FalseTrueTrueNone632015TrueFalseFalseFalseTrueTrueFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseTrueTrueFalseTrueFalseFalseFalseFalseFalse
5True1FalseTrueFalseTruehouse1470120671301215TrueTrueTrueNone6272015FalseFalseTrueTrueTrueFalseFalseFalseFalseFalseFalseFalseTrueFalseTrueFalseFalseTrueTrueTrueTrueFalseFalseFalseFalseFalse
6True0FalseFalseFalseFalsehouse126304803334121226429FalseTrueFalseNone2472016TrueFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseTrueTrueTrueFalseFalseTrueTrueFalseTrueFalseFalseFalseFalseFalse
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